The invention relates generally to image processing, and more specifically to a technique for discriminating between finely textured regions and other regions.
There are several applications where it is important to determine quickly whether an image contains regions of fine texture such as halftones and stipples.
For example, problems can arise with printers whose output is at a different resolution from the input scanner, since techniques for converting the resolution are sensitive to texture. A technique that works well on text will generally do poorly on halftones or stipples, and vice versa. If the halftone regions are identified, and if the frequency and screen angle are known, then appropriate techniques can be selected for different portions of the image, and the halftone regions can be resolution converted with an acceptably low level of aliasing artifacts.
Problems also arise for scanners. When scanning a halftoned binary image, beating can occur between the repeat frequency in the image and the size of an integral number of scanned pixels. The result is aliasing, in which a low frequency Moire pattern will be observed in the scanned image. In order to prevent this, it is desirable to use a gray scale scanner, and remove halftoning prior to thresholding. Techniques for removing halftone patterns work best if the halftone frequency and screen angle are known.
It is sometimes necessary for gross segmentation to occur prior to using some segmentation software. Some aspects of segmentation can be accomplished by building a connected component representation of the binary image, such as a line adjacency graph (LAG), and then processing that data structure. However, if one tried to build a LAG from a finely textured screen of appreciable size (say 2 inches square or larger), the storage requirements and computational time might well be excessive. Similarly, it is necessary for segmentation to occur prior to using recognition software. If a halftoned region of a binary image were sent to OCR or graphics vectorization software, it could break the program.
Depending on the threshold setting and resolution of the input scanners, and on the quality of the output printer and number of copy generations, a printed version of the binary image of a regular textured region may show little or none of the details of the texturing of the original binary image. Because of scanning and printing operations, the contrast at the 1-5 line pairs per mm size is often greatly increased, with dark textures becoming solid black, and light textures becoming much lighter or even white (perhaps with some random dot noise). Thus, in many situations, it cannot be assumed that evidence of the original texturing will remain in the binary image under analysis.